As an important device for disaster rescue, unmanned aerial vehicles(UAVs) are usually limited by their batteries and computational power to perform complex computational tasks during the rescue process. In response to the above situation, it is proposed that the disaster rescue makes parked vehicles collaboratively perform the application tasks generated by UAVs. We organize parked vehicles into parking clusters.. Then, a task scheduling model of deep reinforcement learning(DRL) is built, in which multiple vehicles in the parking cluster are chosen to execute task data together to ensure the quality of experience(QoE) of UAVs. The experimental results demonstrate that the proposed strategy achieves a lower execution cost and higher completion rate compared with other offloading strategies.
Unmanned Aerial Vehicle Data Uploading Using Parking Resources After Disaster Rescue
14.07.2023
1703769 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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